The purpose of this monograph is to give the broad aspects of nonlinear identification and control using neural networks. It consists of three parts:- an introduction to the fundamental principles of neural networks;- several methods for nonlinear identification using neural networks;- various techniques for nonlinear control using neural networks.A number of simulated and industrial examples are used throughout the monograph to demonstrate the operation of nonlinear identification and control techniques using neural networks. It should be emphasised that the methods and systems of nonlinear control have not progressed as rapidly as those for linear control. Comparatively speaking, at the present time, they are still in the development stage. We believe that the fundamental theory, various design methods and techniques, and several applications of nonlinear identification and control using neural networks that are presented in this monograph will enable the reader to analyse and synthesise nonlinear control systems quantitatively.

Helicopters, aircraft and missiles are just some of the practical multivariable control systems to which eigenstructure assignment has been applied in recent years. Liu and Patton offer a uniquely integrated introduction to eigenstructure assignment theory and techniques for multi-input multi-output control system design. Features include:
* Introduction to the Eigenstructure Assignment Toolbox for use with MATLAB (examples available via the Internet) providing engineers with a powerful set of tools for the design of multivariable systems
* Broad coverage including the principle of eigenstructure assignment, basic, insensitive, robust and multiobjective eigenstructure assignment for multirate sampled-data systems, descriptor systems and fault detection systems
* Description of the majority of known eigenstructure assignment methods for both state and output feedback control offering the reader a concise reference
* Combination of time-domain and frequency-domain performance specifications for robust control design
Postgraduates and researchers studying control engineering will appreciate the combination of mathematical theory and practical issues. Control engineers, particularly those working in the aerospace industry, will profit from the detailed application sections which relate eigenstructure assignment to real industrial problems.

The purpose of this monograph is to give the broad aspects of nonlinear identification and control using neural networks. It uses a number of simulated and industrial examples throughout, to demonstrate the operation of nonlinear identification and control techniques using neural networks.